Model-based human body estimation.
Most existing methods on human body estimation adopt a model-based representation.
For instance, SMPL [1]} is a widely-used statistical human body model that maps a set of pose \(\theta \in \mathbb {R}^{72}\)  and shape \(\beta \in \mathbb {R}^{10}\)  parameters to a 3D human mesh \(V \in \mathbb {R}^{6890\times 3}\) .
In SMPL, \(\theta \)  represents the axis-angle 3D rotations of 24 joints, and \(\beta \)  is the top-10 PCA coefficients of a statistical human shape space.
Early methods iteratively optimize the SMPL parameters to fit the estimated 2D keypoints [2]} or silhouettes [3]}.
Several recent works [4]}, [5]}, [6]}, [7]}, [8]}, [9]} train a deep neural network to directly regress SMPL parameters from an input image.
However, the SMPL representation is not always informative enough for a network to learn as it embeds the articulated body shapes in a low dimensional space.
The regression-based methods often fail on truncation and occlusion cases since the networks tend to make holistic predictions based on certain body parts only [10]}.
Instead, we show that localizing 3D vertices is a more suitable task to learn for such scenarios.
The network needs to learn the relationship between the parameters and the shape as well in order to estimate accurate SMPL parameters.
